Abstract
In this paper we present a new approach for the detection of defects in random colour textures. This approach is based on the use of the T2 statistic and it is derived from the MIA strategy (Multivariate Image Analysis) developed in recent years in the field of applied statistics. PCA analysis is used to extract a reference eigenspace from a matrix built by unfolding the RGB raw data of defect-free images. The unfolding is performed compiling colour and spatial information of pixels. New testing images are also unfolded and projected onto the reference eigenspace obtaining a score matrix used to compute the T2 images. These images are converted into defect maps which allow the location of defective pixels. Only very few samples are needed to perform unsupervised training. With regard to literature, the method uses one of the simplest approaches providing low computational costs.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
References
Latif-Amet, L., Ertuzun, A., Ercil, A.: An efficient method for texture defect detection: Subband domain co-occurrence matrices. Image and Vision Computing 18, 543–553 (2000)
Cohen, F., Fan, Z., Attali, S.: Automated inspection of textile fabrics using textural models. IEEE Transactions on Pattern Analysis and Machine Intelligence 13, 803–809 (1991)
Escofet, J., Navarro, R., Millán, M., Pladellorents, J.: Detection of local defects in textile webs using Gabor filters. Optical Engineering 37, 2297–2307 (1998)
Wen, W., Xia, A.: Verifying edges for visual inspection purposes. Pattern Recognition Letters 20, 315–328 (1999)
Tsai, D., Hsiao, B.: Automatic surface inspection using wavelet reconstruction. Pattern Recognition 34, 1285–1305 (2001)
Kyllönen, J., Pietikänien, M.: Visual inspection of parquet slabs by combining color and texture. In: IAPR Workshop on Machine Vision Applications (MVA 2000), pp. 187–192 (2000)
Tsai, D., Tsai, Y.: Defect detection in textured surfaces using color-ring projection correlation. Machine Vision and Applications 13, 194–200 (2003)
Song, K.Y., Kittler, J., Petrou, M.: Defect detection in random colour textures. Image and Vision Computing 14, 667–683 (1996)
Mäenpää, T., Viertola, J., Pietikäinen, M.: Optimising colour and texture features for real-time visual inspection. Pattern Analysis and Applications 6, 169–175 (2003)
Xie, X., Mirmehdi, M.: Texture exemplars for defect detection on random textures. In: Singh, S., Singh, M., Apte, C., Perner, P. (eds.) ICAPR 2005. LNCS, vol. 3687, pp. 404–413. Springer, Heidelberg (2005)
Xie, X., Mirmehdi, M.: Localising surface defects in random colour textures using multiscale texem analysis. In: International Conference on Image Processing (ICIP 2005), pp. 1124–1127 (2005)
Geladi, P., Granh, H.: Multivariate Image Analysis. Wiley, Chichester, England (1996)
Bharati, M.H., MacGregor, J.F.: Texture analysis of images using Principal Component Analysis. In: SPIE/Photonics Conference on Process Imaging for Automatic Control, pp. 27–37 (2000)
Prats-Montalbán, J.M., Ferrer, A.: Integration of spectral and textural information in Multivariate Image Analysis. Part 1: On-line process monitoring for visualizing defects on image data. Journal of Chemometrics (submitted)
Prats-Montalbán, J.M., Ferrer, A.: Integration of spectral and textural information in Multivariate Image Analysis. Part 2: Optimisation of classification models. Journal of Chemometrics (submitted)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2006 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
López, F., Prats, J.M., Ferrer, A., Valiente, J.M. (2006). Defect Detection in Random Colour Textures Using the MIA T2 Defect Maps. In: Campilho, A., Kamel, M. (eds) Image Analysis and Recognition. ICIAR 2006. Lecture Notes in Computer Science, vol 4142. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11867661_68
Download citation
DOI: https://doi.org/10.1007/11867661_68
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-44894-5
Online ISBN: 978-3-540-44896-9
eBook Packages: Computer ScienceComputer Science (R0)